Abnormality Pattern Detection Using Multi-Outlier Similarity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing abnormality detection methods based on outlier detection techniques can only perform binary judgments of normal or abnormality and struggle to identify specific abnormality patterns, leading to difficulties in determining the type of abnormality and requiring engineer examination, as different methods detect different patterns and lack comprehensive coverage.
Innovation Solution
Calculating the similarity between deviation sensitivity information and deviation inclination information for each abnormality pattern to suitably detect and specify the type of abnormality, using a system that integrates multiple outlier detection methods and stores sensitivity information systematically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple outlier detection methods are used to improve abnormality detection coverage, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the abnormality detection task by dividing it into multiple specialized outlier detection methods, each targeting specific abnormality patterns. Instead of using one complex method to detect all abnormalities, the system divides the detection space into multiple segments (different abnormality patterns) and assigns specialized detection methods to each segment, thereby improving overall detection capability while managing complexity through structured division
Solution Approach 2:
The patent creates a universal abnormality detection system that integrates multiple outlier detection methods into a single platform capable of detecting various types of abnormalities. The system performs multiple functions (different detection algorithms) within one unified framework, allowing it to handle diverse abnormality patterns without requiring separate systems for each detection method
2Ease of operation
If binary judgment method is used to simplify abnormality detection, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The patent segments the abnormality information by categorizing it into distinct abnormality patterns. Instead of providing a single binary judgment, the system divides the information output into multiple pattern categories, each representing a specific type of abnormality. This segmentation preserves information about the nature and type of abnormalities while maintaining operational simplicity through structured classification
Solution Approach 2:
The patent transitions from a one-dimensional binary judgment (normal/abnormal) to a multi-dimensional classification system that adds the dimension of abnormality pattern types. By introducing this additional dimension, the system preserves rich information about different abnormality patterns while maintaining ease of operation through automated pattern recognition and classification
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A deviation inclination calculation unit (22) calculates a deviation score by using evaluation data obtained from a subject apparatus as an input, in each of a plurality of outlier detection methods specifying data deviated from other data from among subject data, and calculating deviation scores indicating deviation degrees of the data specified, and calculates deviation inclination information from the deviation scores calculated. An abnormality detection unit (23) calculates, for each abnormality pattern, a similarity degree between deviation sensitivity information indicating sensitivity for each of a plurality of abnormality patterns with respect to each of the plurality of outlier detection methods, and the deviation inclination information calculated, and detects an abnormality of the subject apparatus.